The present invention discloses an integrated karaoke device, including a microphone and a sound box. The sound box includes a sound chamber containing a loudspeaker. The integrated karaoke device further includes a connecting part made of flexible material that is fixedly connected between a bottom of the microphone and a top of the sound chamber. The use of a flexible material connecting part rather than a solid structural part reduces the transmission to the microphone of sound vibration generated by the sound box. The beneficial effect of implementing the present invention is that a flexible material connecting part is fixedly connected between the bottom of the microphone and the top of the sound chamber, and meanwhile, a gap is kept between the bottom of a printed circuit board (PCB) and the top of the sound chamber; and because the flexible material connecting part can effectively reduce sound vibration, the transmission of vibration to the microphone from the sound box can be effectively eliminated, thereby preventing squealing, and enabling integration of the microphone and the sound box for use in karaoke.

A device for automatically generating a real time accompaniment of input music data includes a music input that receives music data. A music analyzer analyzes received music data to obtain a music data description including one or more characteristics of the analyzed music data. A query generator generates a query to a music database including music patterns and associated metadata including one or more characteristics of the music patterns, the query being generated from the music data description and from an accompaniment description describing preferences of the real time accompaniment and/or music rules describing general rules of music. A query interface queries the music database using a generated query and receives a music pattern selected from the music database by use of the query. A music output outputs the received music pattern.

In an electronic device for an electronic musical instrument, a determination grace period during which a plurality of operations on the electronic musical instrument by a user are determined to be simultaneously performed for the first section is set based on the data included in a first section of the song having a plurality of sections. Automatic accompaniment is advanced from the first section to the next second section when a user operation of the electronic musical instrument is detected outside of the determination grace period for the first section during the playback of the first section of the accompaniment, and automatic accompaniment is not advanced from the first section to the second section when the user operation of the electronic musical instrument is detected within the determination grace period for the first section during the playback of the first section of the accompaniment.

A method for synchronizing a pre-recorded music accompaniment to a music playing of a user. The music playing is captured by at least one microphone, which delivers an input acoustic signal feeding a processing unit, which includes a memory for storing data of the music accompaniment and provides an output acoustic signal based on the music accompaniment data to feed at least a loudspeaker playing the music accompaniment. The processing unit analyses the input acoustic signal to detect musical events and music tempo, compares the detected musical events to the music accompaniment data to determine at least a lag between the timings of the detected musical events and the musical events of the music accompaniment, and adapts a timing of the output acoustic signal based on the lag and a synchronization function calculated from a temporal variable, the user's music tempo, and the duration of compensation of the lag.

A system, method and computer product for training a neural network system. The method comprises inputting an audio signal to the system to generate plural outputs f(X, Θ). The audio signal includes one or more of vocal content and/or musical instrument content, and each output f(X, Θ) corresponds to a respective one of the different content types. The method also comprises comparing individual outputs f(X, Θ) of the neural network system to corresponding target signals. For each compared output f(X, Θ), at least one parameter of the system is adjusted to reduce a result of the comparing performed for the output f(X, Θ), to train the system to estimate the different content types. In one example embodiment, the system comprises a U-Net architecture. After training, the system can estimate various different types of vocal and/or instrument components of an audio signal, depending on which type of component(s) the system is trained to estimate.

Techniques are provided for implementing multi-track audio analysis. In some instances, a plurality of audio tracks are received and combined into a single audio file. A collection of audio file characteristics are extracted from the single audio file. In some examples, the collection of audio file characteristics are associated with of the plurality of audio tracks and the single audio file. Audio characteristic edits are received for revising the collection of audio file characteristics. Metadata associated with each of the plurality of audio tracks and for the single audio file are updated based at least in part on the audio characteristic edits.

An electronic device, a data usage method, and an electronic device program are provided. In an electronic musical instrument, in a case where a livelock flag has been turned on by an operation on a setting key by a user, a sound data is validated or invalidated according to a usable or unusable state of the sound data stored in a content state table immediately before the livelock flag has been turned on.

An object is to improve accuracy of prediction of a result of scoring relating to user's singing of a new musical piece. A prediction device is a device that acquires a result of scoring relating to a user's singing of a musical piece in the past for each section of the musical piece in time, acquires pitch information representing pitches of notes configuring the musical piece and aligned in a time series in the section, builds a learning model predicting a result of scoring relating to the user's singing of a musical piece from the pitch information using the result of scoring and the pitch information as training data, and acquires a result of scoring relating to the user's singing of a new musical piece on the basis of an output of the learning model by inputting the pitch information about the new musical piece to the learning model.

A motor vehicle includes a loudspeaker, a voice recognition module, and a microphone producing a microphone signal based upon words uttered by a human passenger within a passenger compartment of the motor vehicle. An electronic processor is communicatively coupled to the microphone, the loudspeaker, and the voice recognition module. The electronic processor receives the microphone signal and communicates with the voice recognition module to thereby ascertain the words uttered by the human passenger. The electronic processor retrieves Karaoke music corresponding to the ascertained words uttered by the human passenger, and plays the Karaoke music on the loudspeaker.

In one embodiment, an apparatus comprising a first audio looping device is provided. The first audio looping device is electrically coupled to a computing device. The first audio looping device is programmed to receive a first audio signal from a musical instrument and to store the first audio signal. The first audio looping device is further programmed to playback the stored first audio signal as a first audio loop a number of times and to transmit the first audio loop to a second audio looping device via the first computing device. The first audio looping device is further programmed to receive a second audio loop from the second audio looping device via the computing device.